Abstract
An important point in pattern recognition and image analysis is the study of properties of the shapes used to represent an object in an image. Particularly, an interesting measure of a shape is its level of complexity, a value that can be obtained from its fractal dimension. Many methods were developed for estimating the fractal dimensions of shapes but none of these are efficient for every situation. This work proposes a novel approach to estimate the fractal dimension from shape contour by using Curvature Scale Space (CSS). Efficiency of the technique in comparison to the well-known method of Bouligand-Minkowski. Results show that the use of CSS yields fractal dimension values robust to several shape transformations (such as rotation, scale and presence of noise), so providing interesting results for a process of classification of shapes based on this measure.
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© 2009 Springer-Verlag Berlin Heidelberg
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Backes, A.R., Florindo, J.B., Bruno, O.M. (2009). A Novel Approach to Estimate Fractal Dimension from Closed Curves. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_31
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DOI: https://doi.org/10.1007/978-3-642-03767-2_31
Publisher Name: Springer, Berlin, Heidelberg
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